DCRNN [1]} extends DCNN with RNN so that it also captures time dependencies within the data. Our work is closely related and builds on top of DCRNN. The main difference is that DCRNN assumes that the adjacency matrix used in random walks is static. However, this assumption might not always hold and
we propose to learn an adaptive adjacency matrix at each time stamp using attention mechanism that considers graph topology.
Multi-task learning [2]} has also been applied for correlated time series forecasting, where both a CNN and a RNN are combined to both forecast future values and reconstruct historical values [3]}. Our work resembles [4]} and [5]}. The main difference is that our model tries to learn a dynamic adjacency matrix which can afterwards be used with any type of graph-RNN like structure to update each node embedding by taking into account its neighbours offering more flexibility.
